Artificial Intelligence-Driven Insights in Plant Pathology

Artificial Intelligence (AI) is rapidly transforming the field of plant pathology by offering innovative approaches to decipher the intricacies of plant–pathogen interactions. As plant diseases continue to threaten global food security and ecosystem balance, researchers are increasingly turning to AI to model host–pathogen dynamics, predict disease outbreaks, and guide targeted interventions. Recent progress in AI-driven modeling has revealed patterns in genetic, epigenetic, and ecological data that were previously inaccessible with traditional methods. Notwithstanding these advances, a significant challenge remains: integrating computational insights with experimental validation to ensure reliability and biological relevance.

Recent studies have demonstrated that AI can outperform classical techniques in identifying resistance genes, predicting pathogen virulence factors, and optimizing the deployment of resistant crop varieties. Nevertheless, there is an ongoing debate regarding the interpretability, scalability, and practical transferability of AI-generated predictions into field and laboratory settings. Although some research has successfully utilized AI to inform the design of laboratory assays or to guide field sampling, the iterative loop where AI predictions are continuously tested and refined through experimentation is still underexplored. Addressing this disconnect is essential for maximizing the value and impact of AI in real-world plant pathology.

This Research Topic aims to catalyze the integration of computational and biological approaches by promoting studies that explicitly test AI-generated hypotheses through direct experimentation. By fostering research that bridges models with laboratory, greenhouse, or field data, this topic seeks to accelerate discovery and validation of mechanisms underlying plant–pathogen interactions and support the development of practical strategies for disease management.

The scope of this Research Topic encompasses original investigations that connect AI models to empirical validation, but is limited to work that explicitly addresses the impact or utility of AI through experimental feedback. We encourage submissions that illuminate the iterative relationship between computational innovation and biological discovery, including, but not limited to, the following themes:

- AI-driven modeling of plant immune responses
- Applications of AI in genetic and epigenetic analyses
- Studies linking computational predictions of pathogen evolution with resistance assays
- Integration of AI tools into precision agriculture workflows
- Automated experimental design and adaptive laboratory or field experimentation driven by AI
- Methodological advances in establishing experimental feedback loops for model refinement
- Perspectives on the challenges and opportunities in merging AI and biological experimentation

Appendix: We welcome original research articles, reviews, and perspectives within this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial intelligence in plant pathology, Plant–pathogen interactions, AI-driven disease prediction, Computational biology in agriculture, Plant immune response modeling, Machine learning in crop protection, Precision agriculture AI, Experimental validation of AI predictions, Genetic analysis using AI, Sustainable agriculture technology

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.